Corrado Priami

Application of a stochastic name-passing calculus to representation and simulation of molecular processes

Abstract We describe a novel application of a stochastic name-passing calculus for the study of biomolecular systems. We specify the structure and dynamics of biochemical networks in a variant of the stochastic π -calculus, yielding a model which is mathematically well-defined and biologically faithful. We adapt the operational semantics of the calculus to account for both the time and probability of biochemical reactions, and present a computer implementation of the calculus for biochemical simulations.

Stochastic π-Calculus

We extend the π-calculus, a model of concurrent processes based on the notion of naming, to cope with performance modelling. The new language is called the stochastic π-calculus (Sπ). We obtain a more expressive language than classical stochastic process algebras because Sπ allows one to describe dynamically reconfigurable or mobile networks. The semantics of Sπ is given in the classical structural operational approach. In order to effectively compute performance measures, we define a stratified transition system that is finitely branching. We give a transition rule to directly yield a continuous time Markov chain from an Sπ specification, with no transition system manipulation.

Beta binders for biological interactions

This paper presents binders and operators, in the process calculi tradition, to reason about biological interactions. Special binders are added to wrap a process just as membranes enclose some living matter and hence to mimick biological interfaces. A few operators are then added to the pi-calculus kernel to describe the dynamics of those interfaces.

Algorithmic systems biology

The convergence of CS and biology will serve both disciplines, providing each with greater power and relevance.

Non-interleaving semantics for mobile processes

Abstract This paper studies causality in the π-calculus. Our notion of causality combines the dependencies given by the syntactic structure of processes with those originated by passing names. Our studies show that two transitions not causally related may however occur in a fixed ordering in any computation, i.e., the π-calculus may implicitly express a precedence between actions. The same partial order of transitions is associated with all the computations that are obtained by shuffling transitions that are concurrent (i.e. related neither by causality nor by precedence). Other non-interleaving semantics are investigated and compared. The presentation takes advantage of a parametric definition of process behaviour given in SOS style that permits us to take almost for free the interleaving theory and tools. Finally, we extend our approach to higher-order π-calculus, enriched with a spawn operation.

The Beta Workbench: a computational tool to study the dynamics of biological systems

We introduce the Beta Workbench (BWB), a scalable tool built on top of the newly defined BlenX language to model, simulate and analyse biological systems. We show the features and the incremental modelling process supported by the BWB on a running example based on the mitogen-activated kinase pathway. Finally, we provide a comparison with related approaches and some hints for future extensions.

The BlenX language: a tutorial

This paper presents a new programming language, BlenX. BlenX is inspired to the process calculus Beta-binders and it is intended for modelling any system whose basic step of computation is an interaction between sub-components. The original development was thought for biological systems. Therefore this tutorial exemplifies BlenX features on biology-related systems.

A new probabilistic generative model of parameter inference in biochemical networks

We present a new method for estimating rate coefficients and level of noise in models of biochemical networks from noisy observations of concentration levels at discrete time points. Its probabilistic formulation, based on maximum likelihood estimation, is key to a principled handling of the noise inherent in biological data, and it allows for a number of further extensions, such as a fully Bayesian treatment of the parameter inference and automated model selection strategies based on the comparison between marginal likelihoods of different models. We developed KInfer (Knowlegde Inference), a tool implementing our inference model. KInfer is downloadable for free at http://www.cosbi.eu.

Modelling biochemical pathways through enhanced π-calculus

We use the π-calculus to model the evolution of biochemical systems, taking advantage of their similarities with global computation applications. First, we present a reduction semantics for the π-calculus from which causality and concurrency can be mechanically derived. We prove that our semantics agrees with the causal definitions presented in the literature. We also extend our semantics to model biological compartments. Then, we show the applicability of our proposal on a couple of biological examples.

Transactions on Computational Systems Biology VI

Property-Driven Statistics of Biological Networks.- On the Computational Power of Brane Calculi.- Analysis of Signalling Pathways Using Continuous Time Markov Chains.- Machine Learning Biochemical Networks from Temporal Logic Properties.- Qualitative Petri Net Modelling of Genetic Networks.- Simulating Bacterial Transcription and Translation in a Stochastic ? Calculus.- Automated Abstraction Methodology for Genetic Regulatory Networks.- P Systems, a New Computational Modelling Tool for Systems Biology.- Equivalence of Metabolite Fragments and Flow Analysis of Isotopomer Distributions for Flux Estimation.- Multiple Representations of Biological Processes.